what technographic data is<\/a>.<\/p>\n\nPredictLeads catalogs 73,189 live technologies in 302 categories and flags evidence found outside website code, such as a CRM named in job descriptions, with the behind_firewall<\/code> field.<\/p>\n\nTechnographic vs. Firmographic Data: A Side-by-Side Comparison<\/h2>\n\n Firmographic data tells you whether an account fits your ICP; technographic data tells you whether its stack fits your product. The table goes past definitions to how each type is sourced, how it goes stale, and how you check it.<\/p>\n\n
\n
\nTechnographic vs. firmographic data across nine dimensions<\/caption>\n \n\nDimension<\/th>\n Firmographic data<\/th>\n Technographic data<\/th>\n<\/tr>\n<\/thead>\n \n\nCore question<\/strong><\/td>\nCan this account buy from us?<\/td>\n Can this account use what we sell?<\/td>\n<\/tr>\n \nTypical fields<\/strong><\/td>\nIndustry code, employee range, revenue range, location, parent company<\/td>\n Technology, category, source, first and last seen dates, source count<\/td>\n<\/tr>\n \nWhere it comes from<\/strong><\/td>\nCompany websites, filings, registries, and estimates<\/td>\n Script tags, cookies, DNS records, IP ranges, and job descriptions<\/td>\n<\/tr>\n \nPace of change<\/strong><\/td>\nSlow: headcount and revenue move over quarters; industry rarely changes<\/td>\n Continuous: tools appear and stop being detected all year<\/td>\n<\/tr>\n \nWhat stale looks like<\/strong><\/td>\nAn employee range that lags a hiring push or a layoff<\/td>\n A last seen date that stopped advancing<\/td>\n<\/tr>\n \nMain error source<\/strong><\/td>\nEstimated revenue and headcount for private companies<\/td>\n Internal tools with no public signature; ambiguous job-description mentions<\/td>\n<\/tr>\n \nHow to verify<\/strong><\/td>\nCompare with filings or the company’s own site<\/td>\n Open the source URL; check source count and last seen date<\/td>\n<\/tr>\n \nChange worth watching<\/strong><\/td>\nHeadcount growth, acquisitions, rebrands<\/td>\n New detections and tools no longer detected<\/td>\n<\/tr>\n \nBest used for<\/strong><\/td>\nMarket sizing, territories, routing, ICP fit<\/td>\n Displacement, integration targeting, stack-specific messaging<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\nIntent and contact data answer two more questions, what an account is researching and who works there. Our technographic data guide compares all four types in one table.<\/p>\n\n
Where Each Data Type Fails on Its Own<\/h2>\n\n Each data type produces a list that looks right and still wastes rep time, in a different way. Take a vendor whose product integrates with cloud data warehouses.<\/p>\n\n
A Firmographic-Only List<\/h3>\n\n The vendor pulls US software companies with 201 to 1,000 employees. Every account fits the ICP on paper. The list still includes companies with no evidence of any data warehouse, and companies with evidence of a warehouse the product does not support. Firmographics confirm an account can buy; they cannot tell you whether it can use the product.<\/p>\n\n
A Technographic-Only List<\/h3>\n\n The vendor pulls every company with a Snowflake detection instead. Now the list mixes a six-person consultancy whose job descriptions mention Snowflake for client work, a 40,000-employee enterprise with a procurement cycle the sales team is not staffed for, and companies outside every territory. Technology evidence shows fit with the product, not fit with your go-to-market.<\/p>\n\n
PredictLeads tags job-description evidence with behind_firewall<\/code> and department_onet_codes<\/code>, so you can see which team asked for a tool before treating a mention as usage.<\/p>\n\nHow to Combine Technographic and Firmographic Data<\/h2>\n\n Combine them as sequential filters: one narrows the universe, the other qualifies it, and a timing signal decides who goes first.<\/p>\n\n
Which Filter to Apply First<\/h3>\n\n Start with whichever filter is more restrictive for your product: firmographics when you can sell to any stack, technographics when your product depends on one.<\/p>\n\n
\n
\nWhich filter to apply first, by go-to-market scenario<\/caption>\n \n\nScenario<\/th>\n Start with<\/th>\n Then apply<\/th>\n<\/tr>\n<\/thead>\n \n\nYour product works with any stack<\/td>\n Firmographic: size, industry, region<\/td>\n Technographic: tailor messaging to the tools detected<\/td>\n<\/tr>\n \nYour product integrates with one platform<\/td>\n Technographic: companies with evidence of that platform<\/td>\n Firmographic: size and region fit<\/td>\n<\/tr>\n \nYou are displacing a competitor<\/td>\n Technographic: competitor detected with a recent last seen date<\/td>\n Firmographic fit, then timing signals<\/td>\n<\/tr>\n \nYou are sizing a market<\/td>\n Firmographic: the full addressable universe<\/td>\n Technographic: share of accounts with evidence of each tool in the category<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\nStarting from a technology is a reverse lookup; our guide to finding every company using a technology by name<\/a> walks through it. For switch-focused lists, use the competitor displacement playbook<\/a>.<\/p>\n\nA Four-Step Workflow<\/h3>\n\n\n\nFour steps to combine firmographic and technographic data<\/title>\n<desc id=\"pl-flow-desc\">Flow diagram with four steps: 1, set the firmographic floor by industry, size, revenue, and region; 2, add the technographic qualifier, a tool or rival seen recently; 3, add a timing signal such as hiring, funding, or a news event; 4, verify sources and route each segment to matching messaging.<\/desc>\n<defs>\n<marker id=\"pl-flow-arrow\" viewBox=\"0 0 10 10\" refX=\"9\" refY=\"5\" markerWidth=\"7\" markerHeight=\"7\" orient=\"auto\"><path d=\"M0,0 L10,5 L0,10 z\" fill=\"#9a78b0\"\/><\/marker>\n<\/defs>\n<rect x=\"0\" y=\"0\" width=\"720\" height=\"230\" rx=\"14\" fill=\"#f3ecf8\"\/>\n<g font-family=\"'Poppins','Segoe UI',system-ui,sans-serif\">\n<text x=\"24\" y=\"38\" font-size=\"19\" font-weight=\"800\" fill=\"#3a2f4d\">Firmographic fit, then stack fit, then timing<\/text>\n<rect x=\"24\" y=\"64\" width=\"150\" height=\"138\" rx=\"12\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n<circle cx=\"50\" cy=\"90\" r=\"14\" fill=\"#76508e\"\/>\n<text x=\"45\" y=\"95\" font-size=\"14\" font-weight=\"700\" fill=\"#ffffff\">1<\/text>\n<text x=\"36\" y=\"128\" font-size=\"15\" font-weight=\"700\" fill=\"#5b3d70\">Firmographic<\/text>\n<text x=\"36\" y=\"147\" font-size=\"15\" font-weight=\"700\" fill=\"#5b3d70\">floor<\/text>\n<text x=\"36\" y=\"172\" font-size=\"12\" fill=\"#3a2f4d\">Industry, size,<\/text>\n<text x=\"36\" y=\"188\" font-size=\"12\" fill=\"#3a2f4d\">revenue, region<\/text>\n<line x1=\"176\" y1=\"133\" x2=\"196\" y2=\"133\" stroke=\"#9a78b0\" stroke-width=\"2\" marker-end=\"url(#pl-flow-arrow)\"\/>\n<rect x=\"198\" y=\"64\" width=\"150\" height=\"138\" rx=\"12\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n<circle cx=\"224\" cy=\"90\" r=\"14\" fill=\"#76508e\"\/>\n<text x=\"219\" y=\"95\" font-size=\"14\" font-weight=\"700\" fill=\"#ffffff\">2<\/text>\n<text x=\"210\" y=\"128\" font-size=\"15\" font-weight=\"700\" fill=\"#5b3d70\">Technographic<\/text>\n<text x=\"210\" y=\"147\" font-size=\"15\" font-weight=\"700\" fill=\"#5b3d70\">qualifier<\/text>\n<text x=\"210\" y=\"172\" font-size=\"12\" fill=\"#3a2f4d\">Tool or rival with<\/text>\n<text x=\"210\" y=\"188\" font-size=\"12\" fill=\"#3a2f4d\">a recent last seen<\/text>\n<line x1=\"350\" y1=\"133\" x2=\"370\" y2=\"133\" stroke=\"#9a78b0\" stroke-width=\"2\" marker-end=\"url(#pl-flow-arrow)\"\/>\n<rect x=\"372\" y=\"64\" width=\"150\" height=\"138\" rx=\"12\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n<circle cx=\"398\" cy=\"90\" r=\"14\" fill=\"#76508e\"\/>\n<text x=\"393\" y=\"95\" font-size=\"14\" font-weight=\"700\" fill=\"#ffffff\">3<\/text>\n<text x=\"384\" y=\"128\" font-size=\"15\" font-weight=\"700\" fill=\"#5b3d70\">Timing<\/text>\n<text x=\"384\" y=\"147\" font-size=\"15\" font-weight=\"700\" fill=\"#5b3d70\">signal<\/text>\n<text x=\"384\" y=\"172\" font-size=\"12\" fill=\"#3a2f4d\">Hiring, funding,<\/text>\n<text x=\"384\" y=\"188\" font-size=\"12\" fill=\"#3a2f4d\">or a news event<\/text>\n<line x1=\"524\" y1=\"133\" x2=\"544\" y2=\"133\" stroke=\"#9a78b0\" stroke-width=\"2\" marker-end=\"url(#pl-flow-arrow)\"\/>\n<rect x=\"546\" y=\"64\" width=\"150\" height=\"138\" rx=\"12\" fill=\"#76508e\"\/>\n<circle cx=\"572\" cy=\"90\" r=\"14\" fill=\"#ffffff\"\/>\n<text x=\"567\" y=\"95\" font-size=\"14\" font-weight=\"700\" fill=\"#76508e\">4<\/text>\n<text x=\"558\" y=\"128\" font-size=\"15\" font-weight=\"700\" fill=\"#ffffff\">Verify<\/text>\n<text x=\"558\" y=\"147\" font-size=\"15\" font-weight=\"700\" fill=\"#ffffff\">and route<\/text>\n<text x=\"558\" y=\"172\" font-size=\"12\" fill=\"#ffffff\">Check sources,<\/text>\n<text x=\"558\" y=\"188\" font-size=\"12\" fill=\"#ffffff\">match the message<\/text>\n<\/g>\n<\/svg>\n<figcaption style=\"font-size:13px;color:#5b3d70;margin-top:6px;\">Apply the more restrictive filter first; the order of steps 1 and 2 can swap.<\/figcaption>\n<\/figure>\n\n<ol>\n<li><strong>Set the firmographic floor.<\/strong> Filter by industry code, employee range, revenue range, and the regions you sell into. This removes accounts that cannot buy, whatever their stack.<\/li>\n<li><strong>Add the technographic qualifier.<\/strong> Keep accounts with evidence of a platform you integrate with or a competitor you replace, and require a recent <code>last_seen_at<\/code>, for example within the last 90 days.<\/li>\n<li><strong>Add a timing signal.<\/strong> Look for hiring in the team that owns the tool, a new financing round, or a news event such as <code>integrates_with<\/code>. Our guide to <a href=\"https:\/\/predictleads.com\/blog\/sales-trigger-events-outbound-timing\/\">sales trigger events and outbound timing<\/a> covers which triggers to watch.<\/li>\n<li><strong>Verify and route.<\/strong> Spot-check source URLs on a sample of accounts, then route each segment to messaging that names the tool it uses. The <a href=\"https:\/\/predictleads.com\/blog\/technographic-lead-scoring-guide\/\">technographic lead scoring guide<\/a> shows how to weight each signal.<\/li>\n<\/ol>\n\n<p>For marketing programs built on the same segments, see <a href=\"https:\/\/predictleads.com\/blog\/account-based-marketing-technographic-data\/\">account-based marketing with technographic data<\/a>.<\/p>\n\n<h2>How PredictLeads Delivers Firmographic and Technographic Data<\/h2>\n\n<p>PredictLeads is a B2B company intelligence and technographic data provider that delivers firmographic and technographic data on the same company record, keyed on the domain, so you join them without fuzzy name matching.<\/p>\n\n<div role=\"group\" aria-label=\"PredictLeads firmographic and technographic coverage, October 2026\" style=\"display:flex;flex-wrap:wrap;gap:12px;background:#f3ecf8;border-radius:14px;padding:16px;margin:20px 0 6px;\">\n<div style=\"flex:1 1 200px;background:#ffffff;border:1px solid #ece0f5;border-radius:12px;padding:14px 16px;\">\n<div style=\"font-size:24px;font-weight:800;color:#76508e;line-height:1.2;\">133,531,593<\/div>\n<div style=\"font-size:14px;color:#3a2f4d;margin-top:4px;\">companies tracked<\/div>\n<\/div>\n<div style=\"flex:1 1 200px;background:#ffffff;border:1px solid #ece0f5;border-radius:12px;padding:14px 16px;\">\n<div style=\"font-size:24px;font-weight:800;color:#76508e;line-height:1.2;\">208<\/div>\n<div style=\"font-size:14px;color:#3a2f4d;margin-top:4px;\">countries covered<\/div>\n<\/div>\n<div style=\"flex:1 1 200px;background:#ffffff;border:1px solid #ece0f5;border-radius:12px;padding:14px 16px;\">\n<div style=\"font-size:24px;font-weight:800;color:#76508e;line-height:1.2;\">2,003,496,359<\/div>\n<div style=\"font-size:14px;color:#3a2f4d;margin-top:4px;\">active technology detections<\/div>\n<\/div>\n<div style=\"flex:1 1 200px;background:#ffffff;border:1px solid #ece0f5;border-radius:12px;padding:14px 16px;\">\n<div style=\"font-size:24px;font-weight:800;color:#76508e;line-height:1.2;\">73,189<\/div>\n<div style=\"font-size:14px;color:#3a2f4d;margin-top:4px;\">live technologies (254,367 ever cataloged)<\/div>\n<\/div>\n<div style=\"flex:1 1 200px;background:#ffffff;border:1px solid #ece0f5;border-radius:12px;padding:14px 16px;\">\n<div style=\"font-size:24px;font-weight:800;color:#76508e;line-height:1.2;\">302<\/div>\n<div style=\"font-size:14px;color:#3a2f4d;margin-top:4px;\">technology categories<\/div>\n<\/div>\n<\/div>\n<p style=\"font-size:13px;color:#5b3d70;margin:0 0 18px;\">PredictLeads firmographic and technographic coverage, October 2026.<\/p>\n\n<ul>\n<li><strong>Firmographics (Companies):<\/strong> NAICS6 codes, revenue ranges, employee ranges, <code>location_data<\/code>, parent and subsidiary companies, and <code>redirects_to<\/code> with a reason (acquisition_merger, locality, or rebranding) when a domain changes. The discover companies endpoint filters by location and size.<\/li>\n<li><strong>Technographics (Technology Detections):<\/strong> five evidence sources, <code>behind_firewall<\/code>, <code>source_count<\/code>, <code>first_seen_at<\/code>, and <code>last_seen_at<\/code>, plus a reverse lookup to <a href=\"https:\/\/docs.predictleads.com\/api_endpoints\/technology_detections_dataset\/retrieve_companies_using_specific_technology_id_or_fuzzy_name\">find companies using a specific technology<\/a>.<\/li>\n<li><strong>Timing on the same record:<\/strong> Job Openings (279.2M+ records since 2018), News Events, and Financing Events.<\/li>\n<li><strong>Delivery:<\/strong> API, flat files, webhooks, and MCP. Our guide to <a href=\"https:\/\/predictleads.com\/blog\/company-data-api-flat-files-webhooks-mcp\/\">company data delivery by API, flat files, webhooks, and MCP<\/a> compares the four.<\/li>\n<\/ul>\n\n<p>PredictLeads collects publicly available information only, keeps personal data out of its company intelligence datasets, and is <a href=\"https:\/\/predictleads.com\/security\">SOC 2 Type II certified<\/a> and GDPR and CCPA compliant. To compare dedicated vendors, see our roundup of <a href=\"https:\/\/predictleads.com\/blog\/technographic-data-providers\/\">technographic data providers<\/a>.<\/p>\n\n<h2>Frequently Asked Questions<\/h2>\n\n<h3>What is the difference between technographic and firmographic data?<\/h3>\n\n<p>Firmographic data describes what a company is: its industry, headcount, revenue range, location, and corporate structure. Technographic data provides evidence of which technologies the company uses or has recently used, such as its CRM, cloud provider, or data warehouse. Firmographics answer whether an account fits your ICP, and technographics answer whether its stack fits your product. PredictLeads delivers both on the same record, keyed on the company domain.<\/p>\n\n<h3>Which matters more for B2B sales in 2026, technographic or firmographic data?<\/h3>\n\n<p>Neither replaces the other; which one leads depends on your product. If you can sell to a company regardless of its tools, firmographics define the list and technographics shape the message. If your product integrates with or replaces a specific tool, technographics define the list and firmographics check fit. PredictLeads supports both starting points, from company discovery by size and location to reverse lookups across 73,189 live technologies.<\/p>\n\n<h3>Can firmographic data tell you which technologies a company uses?<\/h3>\n\n<p>No. Industry and size can hint at likely tools, but two companies with identical firmographics often use different CRMs, clouds, and data warehouses. Only evidence tied to the company itself, such as script tags, DNS records, or job descriptions, shows which tools are in place. PredictLeads links every detection to its source, alongside <code>source_count<\/code> and <code>last_seen_at<\/code>, so you can check it.<\/p>\n\n<h3>How do you combine technographic and firmographic data in a CRM?<\/h3>\n\n<p>Join both on the company domain, then store firmographic fields (industry, employee range, revenue range) next to technology fields (detected tools with first seen and last seen dates). Build segments that require both, such as 201 to 1,000 employees plus evidence of a specific platform observed in the last 90 days. Refresh technographic fields more often than firmographic ones, because stacks change faster. PredictLeads delivers both by API, flat files, webhooks, and MCP, and can push new detections for followed companies by webhook.<\/p>\n\n<h3>What are examples of firmographic and technographic data?<\/h3>\n\n<p>Firmographic examples include a NAICS industry code, an employee range of 501 to 1,000, a revenue range, a headquarters city, and a parent company. Technographic examples include a marketing automation script detected on the website, an email provider found in MX records, and a data warehouse named in job descriptions. Each technographic example should carry a source and first and last seen dates. In PredictLeads, the job-description example would carry a <code>behind_firewall<\/code> value of true.<\/p>\n\n<div style=\"background:#f3ecf8;border:1px solid #ece0f5;border-radius:14px;padding:24px;margin:28px 0;color:#3a2f4d;\">\n<h2 style=\"margin-top:0;\">Ready to see this in your own data?<\/h2>\n<p>Get 100 free API requests when you create an account – no credit card, no sales call.<\/p>\n<p style=\"margin-bottom:0;\"><a href=\"https:\/\/predictleads.com\/sign_up?utm_source=blog&utm_medium=cta&utm_campaign=technographic-vs-firmographic\" style=\"display:inline-block;background:#76508e;color:#ffffff;padding:12px 22px;border-radius:8px;font-weight:700;text-decoration:none;\">Create your free account<\/a><\/p>\n<\/div>\n\n\n\n<script type=\"application\/ld+json\">\n{\n \"@context\": \"https:\/\/schema.org\",\n \"@type\": \"BlogPosting\",\n \"headline\": \"Technographic vs. Firmographic Data: Differences and How to Combine Them (October 2026)\",\n \"description\": \"Technographic vs firmographic data: firmographics describe what a company is, technographics show the tools it uses. 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Technographic data provides evidence of which technologies the company uses or has recently used, such as its CRM, cloud provider, or data warehouse. Firmographics answer whether an account fits your ICP, and technographics answer whether its stack fits your product. PredictLeads delivers both on the same record, keyed on the company domain.\"\n }\n },\n {\n \"@type\": \"Question\",\n \"name\": \"Which matters more for B2B sales in 2026, technographic or firmographic data?\",\n \"acceptedAnswer\": {\n \"@type\": \"Answer\",\n \"text\": \"Neither replaces the other; which one leads depends on your product. If you can sell to a company regardless of its tools, firmographics define the list and technographics shape the message. If your product integrates with or replaces a specific tool, technographics define the list and firmographics check fit. PredictLeads supports both starting points, from company discovery by size and location to reverse lookups across 73,189 live technologies.\"\n }\n },\n {\n \"@type\": \"Question\",\n \"name\": \"Can firmographic data tell you which technologies a company uses?\",\n \"acceptedAnswer\": {\n \"@type\": \"Answer\",\n \"text\": \"No. Industry and size can hint at likely tools, but two companies with identical firmographics often use different CRMs, clouds, and data warehouses. Only evidence tied to the company itself, such as script tags, DNS records, or job descriptions, shows which tools are in place. PredictLeads links every detection to its source, alongside source_count and last_seen_at, so you can check it.\"\n }\n },\n {\n \"@type\": \"Question\",\n \"name\": \"How do you combine technographic and firmographic data in a CRM?\",\n \"acceptedAnswer\": {\n \"@type\": \"Answer\",\n \"text\": \"Join both on the company domain, then store firmographic fields (industry, employee range, revenue range) next to technology fields (detected tools with first seen and last seen dates). Build segments that require both, such as 201 to 1,000 employees plus evidence of a specific platform observed in the last 90 days. Refresh technographic fields more often than firmographic ones, because stacks change faster. PredictLeads delivers both by API, flat files, webhooks, and MCP, and can push new detections for followed companies by webhook.\"\n }\n },\n {\n \"@type\": \"Question\",\n \"name\": \"What are examples of firmographic and technographic data?\",\n \"acceptedAnswer\": {\n \"@type\": \"Answer\",\n \"text\": \"Firmographic examples include a NAICS industry code, an employee range of 501 to 1,000, a revenue range, a headquarters city, and a parent company. Technographic examples include a marketing automation script detected on the website, an email provider found in MX records, and a data warehouse named in job descriptions. Each technographic example should carry a source and first and last seen dates. In PredictLeads, the job-description example would carry a behind_firewall value of true.\"\n }\n },\n {\n \"@type\": \"Question\",\n \"name\": \"Is technographic data a type of firmographic data?\",\n \"acceptedAnswer\": {\n \"@type\": \"Answer\",\n \"text\": \"No, although some providers sell both under the label of company data. Firmographic data describes company attributes, while technographic data describes evidence of the tools a company uses, collected from different sources and changing at a different pace. Treat them as separate fields with separate refresh schedules. PredictLeads delivers them as separate datasets, Companies and Technology Detections, joined on the company domain.\"\n }\n },\n {\n \"@type\": \"Question\",\n \"name\": \"How often does firmographic data change compared with technographic data?\",\n \"acceptedAnswer\": {\n \"@type\": \"Answer\",\n \"text\": \"Firmographic data changes slowly: headcount and revenue move over quarters, while industry and headquarters rarely change. Technographic data changes continuously, as companies adopt tools and as existing detections stop being observed. Refresh technographic fields more often and judge them record by record through the last seen date. PredictLeads crawls high-traffic websites multiple times daily and refreshes job openings about every 36 hours.\"\n }\n },\n {\n \"@type\": \"Question\",\n \"name\": \"Where does firmographic data come from?\",\n \"acceptedAnswer\": {\n \"@type\": \"Answer\",\n \"text\": \"Firmographic data comes from company websites, regulatory filings, business registries, and provider estimates. Public companies disclose revenue and headcount in filings such as annual 10-K reports, while private company figures are usually estimated ranges. Industry is typically mapped to a standard such as NAICS. PredictLeads builds its Companies dataset from public sources and includes NAICS6 codes, revenue ranges, and employee ranges.\"\n }\n },\n {\n \"@type\": \"Question\",\n \"name\": \"Where does technographic data come from?\",\n \"acceptedAnswer\": {\n \"@type\": \"Answer\",\n \"text\": \"Technographic data comes from public evidence matched against known technology signatures: script tags and page code, cookies, DNS records, IP ranges, and job descriptions. Website sources reveal marketing and analytics tools, while job descriptions surface internal systems such as CRMs and data warehouses. PredictLeads uses all five sources and links each detection to its evidence, flagging evidence from outside website code with the behind_firewall field.\"\n }\n },\n {\n \"@type\": \"Question\",\n \"name\": \"What is the difference between technographic, firmographic, and intent data?\",\n \"acceptedAnswer\": {\n \"@type\": \"Answer\",\n \"text\": \"Firmographic data describes what a company is, technographic data provides evidence of which tools it uses, and intent data tracks what it is researching. Firmographics establish fit, technographics establish stack fit and competitive position, and intent suggests timing. None works well alone. PredictLeads adds timing signals of its own through Job Openings, News Events, and Financing Events on the same company record.\"\n }\n },\n {\n \"@type\": \"Question\",\n \"name\": \"Should I filter by firmographics or technographics first?\",\n \"acceptedAnswer\": {\n \"@type\": \"Answer\",\n \"text\": \"Start with the more restrictive filter for your product. If you can sell to any stack, filter by firmographics first and use technographics to tailor messaging. If your product integrates with or replaces a specific tool, start from companies with evidence of that tool, then filter by size and region. PredictLeads supports both starting points through its discover companies and discover technology detections endpoints.\"\n }\n },\n {\n \"@type\": \"Question\",\n \"name\": \"Can I get technographic and firmographic data from one API?\",\n \"acceptedAnswer\": {\n \"@type\": \"Answer\",\n \"text\": \"Yes, if the provider covers both datasets on a shared company key. PredictLeads delivers firmographics through its Companies dataset and technographics through Technology Detections, both keyed on the company domain, across 133,531,593 companies in 208 countries. The same data is available as flat files, webhooks, and through an MCP server, and new accounts get 100 free API requests.\"\n }\n },\n {\n \"@type\": \"Question\",\n \"name\": \"Is firmographic and technographic data GDPR compliant?\",\n \"acceptedAnswer\": {\n \"@type\": \"Answer\",\n \"text\": \"Company-level firmographic and technographic data describes organizations rather than individuals, but you should confirm each provider's posture before a legal review. PredictLeads collects publicly available information only, keeps personal data out of its company intelligence datasets, and is SOC 2 Type II certified and GDPR and CCPA compliant. 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